Exploring collaborative practices for chronic disease management: Results from a new survey to primary care physicians and specialists in Italy
Bibliographic record
Abstract
Background: Non-communicable diseases (NCDs) represent a global health challenge that requires coordination across various healthcare settings. Purpose: This study in Tuscany, Italy, investigates professional integration between primary care physicians (PCPs) and specialists in NCD management. Research Design: A self-developed survey was used to explore professionals’ views on clinical and organizational collaboration, accountability, and service improvement. Study Sample: The study involved primary care physicians (PCPs) and specialists working in the field of NCD management. Data Collection and/or Analysis: The survey gathered data on professionals' perceptions of clinical protocol use, care integration effectiveness, and other aspects of collaboration in NCD management. Results: Findings reveal disparities between PCPs and specialists in clinical protocol use and care integration effectiveness. Conclusions: The study emphasizes the need to reduce bureaucratic obstacles and enhance information sharing. Promoting peer relationships and innovative performance evaluation tools is vital for improving chronic disease management. This survey contributes valuable insights for the development of integrated care models, aiding healthcare decision-makers in enhancing chronic care system performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".